Title

Author

Date of Award

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

First Advisor

Gregor P. Henze

Second Advisor

Michael J. Brandemuehl

Third Advisor

Moncef Krarti

Fourth Advisor

David Bortz

Fifth Advisor

Kevin Houser

Abstract

The purpose of this research was to identify appropriate occupancy and lighting energy models for predicting lighting energy use in buildings and use those results to inform data collection recommendations. A first-order inhomogeneous Markov-chain occupancy model was selected to simulate occupancy patterns in single-occupancy offices. A stochastic lighting action model, modified to bring it in line with current research, was used to simulate the interaction of those occupants with their lighting system assuming certain luminous conditions, including the contribution of daylight. Additionally, the lighting control model was expanded to include a range of user types between a true "active" user who acts in a very energy-aggressive manner and a true "passive" user who uses their lighting independent of daylight conditions and with less regard for wasted energy. The combination of these two models was assessed in a sensitivity analysis using both sensitivity index and total sensitivity for each parameter, which allows their contribution to the combined model's variance to be evaluated. It was found that the mobility parameter in the occupancy model contributed most to the model variance, followed closely by the probability of a switch-off action at departure. The switch-on actions, both at arrival and during occupancy, contributed the least to the model's variance. The combination of models was also applied to assess their ability to predict lighting energy use through comparison to deterministic modeling results and sub-metered lighting energy data from an actual building. Based on the results of that validation assessment, it was found that the limitations in the occupancy model prevented good agreement between measured and predicted performance. Additional model parameters were proposed for integration into the occupancy model, and the revised occupancy model was validated against the sub-metered lighting energy data. The modifications to the occupancy model were found to substantially improve the accuracy of the predictions compared to the sub-metered data.